Neighborhood-Aware Attention Network for Semi-supervised Face Recognition
Qi Zhang, Zhen Lei, Stan Ziqing Li · 2020
Although face recognition has achieved fairly remarkable results in recent years, it heavily relies on large- scale labeled data to train the high-capacity deep convolutional neural networks. It is unrealistic to collect larger labeled datasets to further boost the performance, which requires burdensome and expensive annotation efforts. Meanwhile, there exist numerous unlabeled face images. It is challenging but promising to jointly utilize limited labeled and abundant unlabeled data to obtain higher performance gain, which is the target of semi-supervised learning. In this paper, we propose a bottom- up method, Neighborhood-Aware Attention Network (NAAN), for semi-supervised face recognition. It clusters unlabeled face images by collaboratively predicting pairwise relations based on their neighborhood information, where the neighborhood is defined as a k-hop ego network centered in the given sample called "ego". Considering the different importance of neighbors, we employ the graph attention network to learn the ego's representation. We evaluate our model on two face recognition datasets MegaFace and IJB-A, and it yields favorably comparable performance to the fully-supervised results.